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. 2016 Nov 22;8(1):679–691. doi: 10.18632/oncotarget.13510

Accumulated promoter methylation as a potential biomarker for esophageal cancer

Xianzhen Peng 1,2,#, Hengchuan Xue 3,#, Lingshuang Lü 1,#, Peiyi Shi 1, Jianping Wang 3, Jianming Wang 1,4,5
PMCID: PMC5352188  PMID: 27893424

Abstract

We performed a two-stage molecular epidemiological study to explore DNA methylation profiles for potential biomarkers of esophageal squamous cell carcinoma (ESCC) in a Chinese population. Infinium Methylation 450K BeadChip was used to identify genes with differentially methylated CpG sites. Sixteen candidate genes were validated by sequencing 1160 CpG sites in their promoter regions using the Illumina MiSeq platform. When excluding sites with negative changes, 10 genes (BNIP3, BRCA1, CCND1, CDKN2A, HTATIP2, ITGAV, NFKB1, PIK3R1, PRDM16 and PTX3) showed significantly different methylation levels among cancer lesions, remote normal-appearing tissues, and healthy controls. PRDM16 had the highest diagnostic value with the AUC (95% CI) of 0.988 (0.965–1.000), followed by PIK3R1, with the AUC (95% CI) of 0.969 (0.928–1.000). In addition, the methylation status was higher in patients with advanced cancer stages. These results indicate that aberrant DNA methylation may be a potential biomarker for the diagnosis of ESCC.

Keywords: esophageal cancer, epigenetics, methylation, next-generation sequencing, diagnosis

INTRODUCTION

Esophageal cancer is one of the most common cancers worldwide, with approximately 456,000 new cases and 400,000 deaths in 2012 [1, 2]. Esophageal squamous cell carcinoma (ESCC) is the most prevalent esophageal cancer in the world, especially in Asian countries [3, 4]. ESCC is highly invasive and rapidly metastatic, often resulting in a poor postoperative quality of life [5, 6]. In spite of clinical advances in the field of oncology, the overall long-term survival rates of ESCC remain dismal [7]. If patients were diagnosed and treated at an early stage, the five-year survival rate after endoscopic mucosectomy could reach 100% [8]. Therefore, there is an urgent need to identify sensitive and specific biomarkers for the early diagnosis of ESCC.

One of the early events that occur during carcinogenesis are the epigenetic changes [9, 10]. Epigenetic modifications cause heritable changes to cells without changes to DNA sequence. Epigenetic modifications, such as methylation, histone modifications, DNA replication timing, nucleosome positioning, or heterochromatization, result in selective gene expression or repression [9, 11]. DNA methylation is one of the most extensively characterized epigenetic modifications [12, 13]. Aberrant DNA methylation has been associated with various human diseases, including cancer [14], autoimmune diseases [15], mental illness [16], and cardiovascular diseases [17]. Large-scale methylation analysis of human genomic DNA may provide a better understanding of the molecular mechanisms involved in the esophageal carcinogenesis [18].

In this epidemiological study, we analyzed the impact of aberrant DNA methylation levels on the clinical and pathological features of ESCC in a Chinese population, and we investigated the methylation profile as a potential biomarker for the diagnosis of esophageal cancer.

RESULTS

Identification of candidate genes

The heat map of hierarchical clustering of methylation according to the data from the Infinium Methylation 450K array is shown in Supplementary Figure 1. Based on diffScore, delta β and gene function, we selected 16 candidate genes (RASSF1, PIK3R1, ITGAV, NFKB1, TAP2, APC, BRCA1, CCND1, CDH1, CDKN2A, BNIP3, HTATIP2, PRDM16, PTEN, PTX3 and SOCS1) for validation (Supplementary Figure S2).

Validation of methylated CpG sites

We collected 43 cancer lesion samples, 43 remote normal-appearing esophageal tissues, and 10 healthy control tissues. The patients included 28 males and 15 females, with the age ranging from 46 to 81 years (Table 1). We also recruited 10 healthy controls, including 7 males and 3 females, with the age ranging from 42 to 74 years (mean ± standard deviation: 58.8 ± 9.2 years). We sequenced 1160 CpG sites in the promoter region of 16 candidate genes. After excluding loci with low calling rate, 961 CpG sites in 15 genes met the requirements for further analysis (Table 2). There were 33.82% (325/961) CpG sites showing significant differences in the distribution of methylation between ESCC and normal esophageal tissues (P < 0.05). The proportion of differentially methylated sites in each gene is shown in Figure 1. There were 195 sites having 2 to 10 fold changes and 58 sites having more than 10 fold changes between ESCC and normal esophageal tissues. 299 out of differentially methylated 325 CpG sites (92 %) had higher methylation level in ESCC samples compared with healthy controls. In addition, 254 CpG sites had significantly different methylation between remote normal-appearing tissues and health controls, and 221 CpG sites had significantly different methylation status between ESCC and remote normal-appearing tissues. The above results are summarized in a Venn diagram in Figure 2. There were 64 CpG sites differentially methylated between these three groups (cancer lesions, remote normal-appearing samples, and health controls). Among them, 54 CpG sites were located in the gene of PRDM16.

Table 1. Clinical characteristics of patients.

No. Gender Age Tumor location Smoking Drinking TNM G stage (histologic grade)
1 Female 78 Lower No No T2N0M0 G3
2 Female 68 Lower No No T1N0M0 G2
3 Male 72 Middle Yes No T1N0M0 G2
4 Male 64 Middle Yes Yes T3N1M0 G2
5 Male 69 Middle No No T2N0M0 G2
6 Male 62 Middle No No T3N0M0 G2
7 Male 57 Middle Yes Yes T4N0M0 G2
8 Female 58 Upper No No T2N1M0 G2
9 Female 73 Upper No No T3N0M0 G2
10 Female 68 Middle No No T3N1M0 G2
11 Female 68 Upper No No T3N1M0 G2
12 Female 69 Middle No No T2N1M0 G2
13 Female 64 Middle No No T1N0M0 G2
14 Male 64 Lower Yes Yes T2N2M0 G2
15 Female 61 Lower No No T3N1M0 G3
16 Male 75 Middle Yes No T3N1M0 G3
17 Male 54 Middle Yes No T3N0M0 G1
18 Female 65 Middle No No T2N0M0 G2
19 Male 54 Lower Yes Yes T3N1M0 G2
20 Male 62 Middle No No T3N1M0 G3
21 Male 78 Middle Yes No T3N1M0 G2
22 Male 63 Middle Yes No T2N0M0 G3
23 Female 76 Upper Yes Yes T1N0M0 G2
24 Male 59 Middle Yes Yes T2N1M0 G3
25 Female 60 Middle No No T2N1M0 G2
26 Male 67 Middle No No T2N0M0 G3
27 Male 60 Middle No No T2N1M0 G3
28 Male 60 Middle No No T3N1M0 G3
29 Male 67 Middle Yes Yes T3N0M0 G2
30 Male 46 Middle Yes Yes T3N1M0 G2
31 Male 81 Middle No Yes T2N0M0 G2
32 Male 61 Middle No Yes T1N1M0 G3
33 Male 46 Middle No No T3N1M0 G3
34 Male 65 Middle Yes Yes T3N1M0 G2
35 Female 70 Middle No No T3N1M0 G3
36 Male 67 Lower No No T1N0M0 G3
37 Female 60 Upper No No T1N0M0 G2
38 Male 64 Lower No Yes T3N0M0 G2
39 Male 77 Lower Yes No T3N1M0 G2
40 Male 74 Middle Yes Yes T3N2M0 G3
41 Male 71 Lower Yes Yes T3N3M0 G3
42 Female 71 Middle No No T1N0M0 G2
43 Male 60 Middle Yes Yes T2N1M1 G2
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Table 2. Sequenced sites of selected genes.

Genes Fragment Start/Stop Size (bp) Number of CpG sites
RASSF1 RASSF1_M1 50377767/50378028 261 21
RASSF1_M2 50378005/50378218 213 23
RASSF1_M3 50378194/50378472 278 27
RASSF1_M5 50375039/50375295 256 23
RASSF1_M6 50374899/50375126 227 19
RASSF1_M7 50374706/50374925 219 29
RASSF1_M9 50374301/50374516 215 13
PIK3R1 PIK3R1_M2 67511168/67511412 244 22
PIK3R1_M4 67511596/67511806 210 23
PIK3R1_M5 67511286/67511520 234 21
PIK3R1_M6 67511047/67511305 258 22
PIK3R1_M7 67512226/67512438 212 18
PIK3R1_M8 67584255/67584471 216 15
ITGAV ITGAV_M1 187454700/187454960 260 21
ITGAV_M2 187454936/187455177 241 32
ITGAV_M3 187455157/187455369 212 21
NFKB1 NFKB1_M2 103422534/103422795 261 37
NFKB1_M3 103422775/103422981 206 23
NFKB1_M4 103423077/103423302 225 18
TAP2 TAP2_M1 32806418/32806681 263 12
APC APC_M2 112073375/112073585 210 15
BRCA1 BRCA1_M1 41275281/41275523 242 18
BRCA1_M3 41275011/41275281 270 11
BRCA1_M4 41275268/41275528 260 12
CCND1 CCND1_M3 69458670/69458890 220 14
CDKN2A CDKN2A-2 21993123/21993331 208 20
CDKN2A-4 21993770/21993957 187 16
CDKN2A-6 21994239/21994504 265 26
CDKN2A-7 21994477/21994700 223 11
CDKN2A_M8 21972954/21973198 244 11
CDKN2A_M9 21974670/21974872 202 15
CDKN2A_M10 21974852/21975095 243 20
BNIP3 BNIP3-1 133795927/133796159 232 10
BNIP3-3 133796371/133796631 260 33
BNIP3-6 133797020/133797250 230 26
BNIP3-7 133797230/133797402 172 16
HTATIP2 HTATIP2-1 20385087/20385355 268 24
HTATIP2-2 20385336/20385546 210 20
PRDM16 PRDM16-1 2983847/2984081 234 14
PRDM16-5 2984736/2984979 243 29
PRDM16-7 2985182/2985386 204 19
PRDM16-8 2985367/2985573 206 16
PRDM16-9 2985553/2985775 222 19
PTEN PTEN-1 89623758/89624026 268 21
PTX3 PTX3-1 157155257/157155524 267 9
PTX3-2 157155500/157155711 211 25
SOCS1 SOCS1-1 11349069/11349310 241 20
SOCS1-3 11349540/11349759 219 31
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Figure 1. Percent of differentially methylated sites in each candidate gene.

Figure 1

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Figure 2. Venn diagram summarizing the differentially methylated sites.

Figure 2

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Red circle indicates differentially methylated sites between cancer and healthy control tissues; yellow circle indicates differentially methylated sites between cancer and remote normal-appearing tissue; green circle indicates differentially methylated sites between remote normal-appearing and healthy control tissues.

Diagnostic value analysis

We further analyzed the cumulative methylation levels by considering multiple CpG sites in each gene. The diagnostic values of selected CpG sites and genes were estimated based on three different models.

Model 1

We calculated the cumulative methylation by summarizing the frequency of all CpG sites in each gene. Nine genes (APC, BNIP3, BRCA1, CCND1, CDKN2A, HTATIP2, ITGAV, PRDM16 and TAP2) showed significantly different cumulative methylation levels among the three groups. The methylation levels of APC, ITGAV, PRDM16 and PTX3 were significantly different between esophageal cancer and healthy control tissues (Table 3). The AUC (95% CI) of each gene in the diagnosis of ESCC is listed in Table 4. The PRDM16 gene showed the highest diagnostic value with the AUC (95% CI) of 0.958 (0.906–1.000), followed by ITGAV, with the AUC (95% CI) of 0.779 (0.651–0.907).

Table 3. Comparison of cumulative methylation levels of multiple CpG sites in each gene using different models.

Gene Model A Model B Model C
Cumulative methylation level F P P Cumulative methylation level F P P Cumulative methylation level F P P
N (n=10) A (n=43) T (n=43) N vs. A A vs. T N vs. T N (n=10) A (n=43) T (n=43) N vs. A A vs. T N vs. T N (n=10) A (n=43) T (n=43) N vs. A A vs. T N vs. T
APC 0.108 0.118 0.199 6.739 0.034 0.355 0.405 0.04 - - - - - - - - - - - - - - - -
BNIP3 6.902 8.833 8.041 9.57 0.008 0.045 0.031 1 1.251 2.505 2.579 18.411 <0.001 <0.001 1 <0.001 1.251 2.505 2.579 18.411 <0.001 <0.001 1 <0.001
BRCA1 22.537 22.669 20.809 14.314 0.001 1 <0.001 0.475 10.075 9.99 9.038 32.385 <0.001 1 <0.001 0.001 0.88 0.898 0.896 6.655 0.036 0.03 1 0.114
CCND1 0.134 0.144 0.133 7.392 0.025 0.265 0.033 1 0.004 0.008 0.008 8.725 0.013 0.013 1 0.017 0.004 0.008 0.008 8.725 0.013 0.013 1 0.017
CDKN2A 5.913 6.941 7.23 15.116 0.001 0.055 0.001 1 1.718 2.183 2.662 10.386 0.006 0.102 0.009 1 0.352 0.905 1.68 20.527 <0.001 <0.001 1 <0.001
HTATIP2 0.363 0.525 0.527 8.63 0.013 0.01 1 0.052 0.082 0.155 0.17 16.578 <0.001 <0.001 1 <0.001 0.082 0.155 0.17 16.578 <0.001 <0.001 1 <0.001
ITGAV 0.614 0.715 0.722 10.101 0.006 0.005 1 0.013 0.029 0.059 0.065 14.9 0.001 0.003 1 <0.001 0.029 0.059 0.065 14.9 0.001 0.003 1 <0.001
NFKB1 0.687 0.777 0.942 1.731 0.421 - - - 0.007 0.042 0.058 14.347 0.001 0.001 1 0.001 0.007 0.042 0.058 14.347 0.001 0.001 1 0.001
PIK3R1 1.291 1.357 1.969 2.422 0.298 - - - 0.057 0.153 0.213 22.693 <0.001 <0.001 0.576 <0.001 0.041 0.142 0.204 25.481 <0.001 <0.001 0.376 <0.001
PRDM16 9.155 13.202 20.756 35.643 <0.001 0.003 0.001 <0.001 6.724 10.686 18.062 36.8949 <0.001 0.003 <0.001 <0.001 5.86 9.867 17.251 37.181 <0.001 0.002 <0.001 <0.001
PTEN 0.381 0.534 1.666 5.182 0.075 - - - 0.374 0.53 1.659 4.886 0.087 - - - 0.374 0.53 1.659 4.886 0.087 - - -
PTX3 0.468 1.053 1.649 0.947 0.623 - - - 0.301 0.222 0.391 6.239 0.044 0.125 1 0.037 0.03 0.222 0.391 6.239 0.044 0.125 1 0.037
RASSF1 2.535 3.482 8.962 3.598 0.165 - - - 1.804 2.71 6.898 2.871 0.238 - - - 1.803 2.71 6.898 2.871 0.238 - - -
SOCS1 1.328 2.321 2.391 3.888 0.143 - - - 0.085 0.276 0.316 5.436 0.066 - - - 0.084 0.276 2.338 5.436 0.066 - - -
TAP2 5.712 5.719 5.362 32.904 <0.001 1 <0.001 0.003 2.369 2.325 2.152 44.501 <0.001 0.301 <0.001 <0.001 - - - - - - - -
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N: healthy control tissues; A: remote normal-appearing tissues; T: cancer tissue

Table 4. Diagnostic values of selected genes for esophageal cancer using different models.

Gene No. of sample Model A Model B Model C
Cases Controls AUC 95% CI P AUC 95% CI P AUC 95% CI P
APC 43 10 0.73 0.535-0.925 0.024 – – – – – –
BNIP3 43 10 0.556 0.388-0.723 0.585 0.856 0.749-0.962 0.001 0.876 0.775-0.978 < 0.001
BRCA1 43 10 0.326 0.180-0.471 0.088 0.13 0.033–0.228 < 0.001 0.712 0.529–0.895 0.039
CCND1 43 10 0.521 0.301–0.741 0.838 0.799 0.648–0.949 0.003 0.817 0.667–0.966 0.002
CDKN2A 43 10 0.437 0.290–0.585 0.539 0.451 0.301–0.601 0.633 0.912 0.832–0.992 < 0.001
HTATIP2 43 10 0.698 0.558–0.837 0.053 0.872 0.766–0.978 < 0.001 0.881 0.779–0.983 < 0.001
ITGAV 43 10 0.779 0.651–0.907 0.006 0.879 0.783–0.975 < 0.001 0.898 0.807–0.988 < 0.001
NFKB1 43 10 0.605 0.430–0.779 0.306 0.855 0.753–0.956 0.001 0.869 0.772–0.966 < 0.001
PIK3R1 43 10 0.635 0.464–0.806 0.187 0.93 0.863–0.998 < 0.001 0.969 0.928–1.000 < 0.001
PRDM16 43 10 0.958 0.906–1.000 < 0.001 0.967 0.921–1.000 < 0.001 0.988 0.965–1.000 < 0.001
PTEN 43 10 0.663 0.491–0.835 0.112 0.66 0.491–0.830 0.117 0.675 0.505–0.845 0.088
PTX3 43 10 0.593 0.430–0.756 0.363 0.737 0.604–0.871 0.02 0.749 0.617–0.880 0.015
RASSF1 43 10 0.681 0.528–0.835 0.076 0.66 0.488–0.833 0.117 0.676 0.502–0.850 0.086
SOCS1 43 10 0.628 0.472–0.784 0.211 0.74 0.600–0.897 0.019 0.752 0.615–0.890 0.014
TAP2 43 10 0.14 0.031–0.248 < 0.001 0.028 0.000–0.066 < 0.001 – – –
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Model 2

By excluding non-significantly differentiated CpG sites, we calculated the cumulative methylation by summarizing the frequency of significant CpG sites in each gene. Eleven genes (BNIP3, BRCA1, CCND1, CDKN2A, HTATIP2, ITGAV, NFKB1, PIK3R1, PRDM16, PTX3 and TAP2) showed significant differences in methylation between groups. The number of differently methylated genes increased to 10 (BNIP3, BRCA1, CCND1, HTATIP2, ITGAV, NFKB1, PIK3R1, PRDM16, PTX3 and TAP2) between ESCC and healthy control tissues (Table 3). The AUC values (95% CI) of each gene in the diagnosis of ESCC are listed in Table 4. Methylation of PRDM16 gene had the highest diagnostic value, with the AUC (95% CI) of 0.967 (0.921–1.000), followed by PIK3R1, with the AUC (95% CI) of 0.930 (0.863–0.998).

Model 3

We further excluded CpG sites with negative correlations and kept 299 sites for analysis. Ten genes (BNIP3, BRCA1, CCND1, CDKN2A, HTATIP2, ITGAV, NFKB1, PIK3R1, PRDM16 and PTX3) had significantly different methylation status among the three groups. The methylation levels of BNIP3, CCND1, CDKN2A, HTATIP2, ITGAV, NFKB1, PIK3R1, PRDM16 and PTX3 were significantly different between esophageal cancer and healthy control tissues (Table 4). The AUC (95% CI) of each gene in the diagnosis of ESCC is listed in Table 4. The methylation of PRDM16 gene showed the highest diagnostic value, with the AUC (95% CI) of 0.988 (0.965–1.000), followed by PIK3R1, with the AUC (95% CI) of 0.969 (0.928–1.000). Compared with findings using model 1 and model 2, the AUC of each gene in model 3 has greatly increased. Especially for BRCA1 and CDKN2A, the AUC increased from less than 0.5 to 0.712 and 0.912, respectively. Based on the model 3, the cumulative methylation level of most genes increased with the histologic changes from normal to normal-appearing tissues and cancer lesions (Figure 3). To avoid false positives caused by multiple comparisons between groups, we used the Bonferroni correction method. Using Bonferroni correction, 49 CpG sites in 4 genes were significant, including 1 site in BNIP3, 1 site in PIK3R1, 46 sites in PRDM16, and 1 site in SOCS1. The cumulative methylation levels of PRDM16 were significantly different among the three groups (F = 38.445, P < 0.001). The AUC of PRDM16 was 0.963 (95% CI: 0.914–1.000).

Figure 3. The cumulative methylation levels of multiple CpG sites in each gene.

Figure 3

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Methylation status and clinical characteristics

The methylation frequency was higher in patients at advanced cancer stages. For example, samples from patients with N1-3 stage had an average cumulative methylation value of 9.56 in RASSF1 gene, which was significantly higher than that in patients at N0 stage (cumulative methylation value: 3.54). For HTATIP2 gene, samples from patients at G1-2 stages also had a significantly higher cumulative methylation level compared with patients at G3 stage (P < 0.05, Figure 4). The cumulative methylation levels of these genes did not correlate with patient's gender (male and female) and age (< 60 and >= 60 years).

Figure 4. The relationship between clinical characteristics and DNA methylation in cancer lesions.

Figure 4

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Protein expression and methylation status

Next, we analyzed protein levels of RASSF1, PIK3R1 and PTEN by immunohistochemistry. In the esophageal cancer lesions, PIK3R1 was expressed in 65.5% (19/29) cases (+: 18; ++: 1; +++: 0), PTEN was expressed in 48.3% (15/31) cases (+: 14; ++: 1; +++: 0), and RASSF1 was expressed in 56.7% (17/30) cases (+: 14; ++: 3; +++: 0). We observed a negative correlation between the methylation level and the IOD, but the coefficient was not significant (RASSF1: r = −0.122, P = 0.521; PIK3R1: r = −0.215, P = 0.264; PTEN: r = −0.095, P = 0.619). The methylation level of RASSF1 gene was significantly higher in samples with negative expression than in samples with positive expression (P = 0.022).

DISCUSSION

When cancer occurs, a massive global hypomethylation is frequently observed, while certain genes can be hypermethylated at the CpG islands [19]. Previous studies have indicated aberrant DNA methylation in esophageal cancer; however, those studies have focused on limited CpG sites [20–22]. In this study, we used a two-stage study design, sequenced 1160 CpG sites in the promoter region of 16 candidate genes, and demonstrated that aberrant DNA methylation can be a potential biomarker for esophageal cancer.

Compared with other methods, such as MSP, Q-PCR, MethyLight or bisulfite pyrosequencing, NGS used in this study can capture full sample diversity with small amounts of DNA. In addition, NGS can enhance epigenetic analyses with high coverage density and flexibility, which help advance our understanding of epigenetics at the genomic level [23]. A fluorescently labeled reversible terminator is utilized in this system, allowing for the accrual of qualitative and quantitative information of nucleic acid at an incredible throughput while incurring relatively limited costs [24].

One of the most robust epigenetic marks found in this study was PRDM16 gene. PRDM16 is located near the 1p36.3 breakpoint, encoding a zinc finger transcription factor and contains an N-terminal PR domain. It is known to be a fusion partner of RPN1, RUNX1 and other genes in hematopoietic malignancies [25]. The malfunction of PRDM16 is related to a poor prognosis of cancer patients [26]. For example, PRDM16 is often methylated in lung cancer cells, with downregulated protein expression [27]. The demethylation drug 5-aza-2′-dC upregulates PRDM16 expression and suppresses growth of lung cancer cells [27].

Other genes with a higher AUC (over 0.9) for distinguishing ESCC were PIK3R1 and CDKN2A. PIK3R1 encodes a p85 regulatory subunit alpha and appears to play a tumor suppressor role because PI3K subunit p85α (p85α) regulates and stabilizes p110α [28]. A previous study has reported that the expression of PRK3R1 negatively correlates with hypermethylation of CpG sites in PIK3R1 [29]. Our results also showed similar negative correlations, although they were not statistically significant; this may be due to the limited sample size. CDKN2A blocks phosphorylation of the Rb protein and inhibits cell cycle progression. CDKN2A is aberrantly methylated in esophageal cancer [30], and is associated with metastatic and invasive phenotypes [31]. Similar CDKN2A methylation patterns have been observed in gastric and nasopharyngeal carcinoma [32, 33]. As the regional lymph node metastasis is associated with the patient's prognosis, the methylation status of these genes might be used to assess the possibility of recurrence and metastasis of ESCC patients and also help to implement proper medications. Moreover, our study shows that the methylation levels of selected genes, such as RASSF1 and HTATIP2, change with the cancer stages, indicating their potential values in the prognosis of ESCC.

There are several limitations in this study. First, the bisulfite conversion efficiency is critical for the accuracy and the reliability of the results. The incomplete conversion of unmethylated cytosine to uracil or inappropriate conversion of methylcytosine to thymine can cause over- or underestimation of the methylation level. It is also noteworthy that the bisulfite conversion technique cannot be used to discriminate the methylated cytosine from 5-hydroxymethylcytosine [34]. Second, the false positives may be caused by multiple comparison when we compared various CpG sites between groups. We used the Bonferroni correction method to adjust for the test level; however, this is an overcorrection when the tests are correlated [35]. Third, aberrant DNA methylation usually occurs somatically in cancer cells and can also be detected in peripheral blood samples [36]. To evaluate the clinical use of aberrant DNA methylation, a blood-based assay is preferable, since it uses a far less invasive procedure.

In conclusion, aberrant DNA methylation is a promising biomarker that has a good predictive value for identifying esophageal cancer in a molecular diagnostic laboratory. The hypermethylation status of PRDM16, PIK3R1, and CDKN2A genes might be used as a potential biomarker for the diagnosis of ESCC.

MATERIALS AND METHODS

Study design

First, we used the Illumina Infinium 450K Methylation Beadchip to construct a genome-wide DNA methylation profile. Then, candidate genes were selected for the validation using the Next-Generation Sequencing (NGS) platform (Illumina MiSeq platform).

Study subjects

This study was approved by the Ethics Committee of Nanjing Medical University. Written informed consent was obtained from all participants. The methods were carried out in accordance with the approved guidelines. Esophageal cancer patients were recruited in the Yangzhong People's Hospital from 2012 to 2016. Yangzhong is an area with high morbidity and mortality rates of the upper digestive tract cancers [37]. The inclusive criteria were: (1) Patients were diagnosed as ESCC with histopathological evidence; (2) All patients were of Chinese Han origin living in Yangzhong longer than five years; (3) Patients underwent esophagectomy and the lesions were eligible for sampling; (4) None of the patients had received preoperative radiotherapy or chemotherapy. Tissues in the center of the cancer lesion and remote normal-appearing esophagus were excised and immediately stored in -80°C freezer. Healthy control esophageal tissues were collected from individuals who had no cancer history and participated in a screening program for upper digestive tract cancers.

DNA extraction

Genomic DNA was extracted from tissues using the QIAmp DNA Mini Kit (Qiagen, Hilden, Germany). The quality and concentration were evaluated with Thermo NanoDrop 2000-1 spectrophotometer (NanoDrop Technologies, Montchanin, DE, USA).

Infinium methylation 450K array

We used the Infinium 450K Methylation Beadchip (Illumina, San Diego, CA, USA) to evaluate the methylation status of five paired tumor samples and corresponding remote normal-appearing esophagus tissues, along with two normal controls from the healthy population.

Next-generation sequencing (NGS)

Primer design and optimization

Genomic regions were analyzed and transformed to bisulfite-converted sequences by gene CpG software. The primers were designed by the Gensky Bio-Tech Co., Ltd. (Shanghai) to amplify regions of interest from the bisulfite converted DNA. Different sets of primers were compared using 1 ng bisulfite modified positive and negative control DNA samples. The final optimized primers are listed in Table 5.

Table 5. Primers designed for multiplex PCR.
Genes Fragment Forward primer Reverse primer
RASSF1 RASSF1_M1 AAGGAGGGAAGGAAGGGTAAG CCAACTCCCRCAACTCAATAAAC
RASSF1_M2 GGGGAGTTTGAGTTTATTGAGTTG CCCAAATAAAATCRCCACAAAAATC
RASSF1_M3 GATTTTTGTGGYGATTTTATTTGG TACATATAAACAACCACCTCTACTCATCT
RASSF1_M5 GGTAAGYGTATAAGAGTGGTTTTTGGT AACAAACCACAATACAAACATTCTC
RASSF1_M6 GATTTAGTTTTTGTTTTATTGGGGTAG ACCCAAACTAACCCAAACTCC
RASSF1_M7 GTTGTTTTAGGTTATTTYGAAAGAAGG CTACCCCAATAAAACAAAAACTAAATC
RASSF1_M8 GTTAGGAGGGTGGGGTTGTTTA CCTTCTTTCRAAATAACCTAAAACAAC
RASSF1_M9 GGTYGGTTTTAGTTATAGTTGGATAATG TAAACAACCCCACCCTCCTAAC
PIK3R1 PIK3R1_M2 GTTTGGGGTTGGTTGAAAGAT CCTAACRAACCCTTCCTACCAC
PIK3R1_M4 TGGAGYGGAGTTGGAGGAAGTAG CACACCCRAAACTACTACTACCTACCTA
PIK3R1_M5 GGAAAYGGGAGTTAGGATGG CAACAACAACCCCRAATATATATACTC
PIK3R1_M6 GTAGYGATTTTGGTTGTAGTTGGAG CCATCCTAACTCCCRTTTCC
PIK3R1_M7 TTTYGTGGTTTTTTAGTTGTAGTTAGG CCAACAACCTACCCAAACTTAAC
PIK3R1_M8 GAAATTTAGTTGGTTTTTTAATGAGGA ACCTCCCCCCAACCTATTC
ITGAV ITGAV_M1 TTGAGAGGTAGGATGGGTGAG TCTTCTCTCRAAACTCCTACTACCTCT
ITGAV_M2 AGGTAGTAGGAGTTTYGAGAGAAGAAG AAACTCAACCCTCTTACCTACCC
ITGAV_M3 GGGGTAGGTAAGAGGGTTGAG ACTCCTCCTCCTTCCAAATCTC
NFKB1 NFKB1_M2 GGGGTAGGAAGAGGAGGTTT AACCRAACCAAACCAATCAAC
NFKB1_M3 GTTGATTGGTTTGGTTYGGTT CCCTACCRAACCCCCACT
NFKB1_M4 GGGAGGAGGTTGATAGTAGTTGAG CACTCCAACCTTCTCACCATC
TAP2 TAP2_M1 GGTGGTTTAYGTTTGTAATTATAGTATTTTG CTCACTCTTATCRCCCAAACTAAAATAC
TAP2_M2 GTTAAGGTTTTTATTTTGGGTTGG TCTCCAATTACAAAACATTCTCCA
TAP2_M3 GGAGTGGGTAGTTATTTGGGTTG CCAACCCAAAATAAAAACCTTAAC
APC APC_M2 GGGTTAGGGTTAGGTAGGTTGTG CATTCTATCTCCAATAACACCCTAAC
BRCA1 BRCA1_M1 GGGAGGAATTTTGTAAAGAAGAGG ACRAACTAAAAAACTCCTCCAACAC
BRCA1_M2 GGGGAGGYGGTAATGTAAAGAT ACCCCTCAACCCCAATATTTA
BRCA1_M3 AGTGATGTTTTGGGGTATTGG AAACTCCTAACCTCATAACCAACC
BRCA1_M4 GAGGTTAGGAGTTTTAGATTAGTTTGATT CCATCCTCTCATACATACCAACC
CCND1 CCND1_M3 TAGGGTTTGATTTTYGTTTGTAGG AAAACCCCAAAAATTCAAACTC
CDH1 CDH1_M1 GGAATTGTAAAGTATTTGTGAGTTTG CTCCTCAAAACCCRAACTTTCT
CDKN2A CDKN2A-2 GGGATATGGAGGGGGAGAT CTTCTTCCTCTTTCCTCTTCCC
CDKN2A-4 AATAAAATAAGGGGAATAGGGGAG CCATCTTCCCACCCTCAA
CDKN2A-6 GTAGTTAAGGGGGTAGGAGTGG ACTACTACCCTAAACRCTAACTCCTCAA
CDKN2A-7 TTGAGGAGTTAGYGTTTAGGGTAGTAGT TCAATAATACTACRAAAACCACATATCTAAATC
CDKN2A_M8 GTTTTTTAGGTTGGAGTGTAATGG TCTATAATCCCAACATTCTAAAAAACC
CDKN2A_M9 TTAGAGGATTTGAGGGATAGGGT AACCAATCAACCRAAAACTCC
CDKN2A_M10 GGAGTTTTYGGTTGATTGGTT CCCAAAAAACCTCCCCTTT
BNIP3 BNIP3-1 GGTAAYGTGGATTTTGAGGTTGT CCATCCTCCCCTTCCRTAC
BNIP3-3 GGTTGYGGGATGTGTTTTAGTT CAAACCTCTACCCCTCRCCC
BNIP3-6 GGTGGGTYGGAGTTGAGYGT TACACCRCRAAAACCCCTTAC
BNIP3-7 GTAAGGGGTTTTYGGGGTGTA CCTCTAAAAAATACCTCCCAATCC
HTATIP2 HTATIP2-1 TTTGGGTGAGTTGAGTTTAGTAGG AAACATCCCACCTTCCCTAA
HTATIP2-2 TTAGGGAAGGTGGGATGTTT ACTACTAACATCACTAAACATACCCCAC
PRDM16 PRDM16-1 GGTAGGGAATGGGGTTGTG CTAAACCTTCTACCTTAAATCCTCC
PRDM16-2 GGAGGATTTAAGGTAGAAGGTTTAG ACTCCTAACCTTACCCTCCCTAC
PRDM16-3 GTAGGGAGGGTAAGGTTAGGAGT AATAACCCRAACCCAAAAACCT
PRDM16-4 AGGTTTTTGGGTTYGGGTTATT AACTAAACCACCTTCRAAAACCC
PRDM16-5 GGGTTTTYGAAGGTGGTTTAGTT CTCCRCCACTACCCAAAC
PRDM16-7 GAGGGGAGAATGTAGGAGAAAAG CTACTACTCCCRCCCCAACC
PRDM16-8 GGTTGGGGYGGGAGTAGTAG CACTTATCTCTCCCCCCTCTC
PRDM16-9 GAGAGGGGGGAGAGATAAGTG CACTATCTTCATCTCCCCAACA
PTEN PTEN-1 TTAGGGAGGGGGTTTGAGT CTCCTCAACAACCAAAAACCTAA
PTX3 PTX3-1 GTAGGTTTGGGYGGGTTGTT TCCAAAACACTAATCAACCTAACCT
PTX3-2 AGGTTAGGTTGATTAGTGTTTTGGA CTCCTTACCTACCRACAACCAA
SOCS1 SOCS1-1 GGGGAGGGTATTTATATGGTTTTA ACTAAAAACCCCRCTACRCCAAC
SOCS1-2 GTTGGYGTAGYGGGGTTTTTAGT CCTACRAATTCTACTAAAAACCCCTAA
SOCS1-3 TTAGGGGTTTTTAGTAGAATTYGTAGG CAATCTCCACAACAACAAAACC
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Bisulfite conversion and multiplex amplification

Genomic DNA (about 400 ng) was subjected to sodium bisulfite modification using EZ DNA Methylation™-GOLD Kit (Zymo Research, Orange, CA, USA) according to the manufacturer's protocols. An un-methylated cytosine was converted to uracil when treated with bisulfite, whereas a methylated cytosine remained as cytosine [38]. A multiplex PCR was performed using the optimized primer sets. A 20 μl PCR reaction mixture was prepared for each reaction, including 1x buffer (TaKaRa, Tokyo, Japan), 3 mM Mg2+, 0.2 mM dNTP, 0.1 μM of each primer, 1U HotStarTaq polymerase (TaKaRa, Tokyo, Japan) and 2 μl of template DNA. The cycling program was 95°C for 2 min; 11 cycles of 94°C for 20 sec, 63°C for 40 sec with a decreasing temperature step of 0.5°C per cycle, 72°C for 1 min; then followed by 24 cycles of 94°C for 20 sec, 65°C for 30 sec, 72°C for 1 min; 72°C for 2 min.

Index PCR and sequencing

PCR amplicons were then diluted and amplified using the indexed primers. Specifically, a 20 μl mixture was prepared for each reaction, including 1x buffer (NEB, MA, USA), 0.3 mM dNTP, 0.3 μM forward primer, 0.3 μM index primer, 1 U Q5TM DNA polymerase (NEB, MA, USA) and 1 μL of diluted template (PCR amplicons from the previous step). The cycling program was 98°C for 30 sec; 11 cycles of 98°C for 10 sec, 65°C for 30 sec, 72°C for 30 sec; 72°C for 5 min. The PCR products (170 bp −270 bp) were separated by agarose electrophoresis and purified using the QIAquick Gel Extraction kit (Qiagen, Hilden, Germany). Libraries from different samples were quantified and pooled together, followed by sequencing on the Illumina MiSeq platform according to the manufacturer's protocols. Sequencing was performed with a 2 × 300 bp paired-end mode. Quality control of sequencing-reads was performed by FastQC (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/). Filtered-reads were aligned back to the reference genome using the Bismark software (http://www.bioinformatics.babraham.ac.uk/projects/bismark/). After reads recalibration withUSEARCH [39], the methylation and haplotype were analyzed using the Perl script.

Immunohistochemistry

Sections (4 μm) of formalin fixed, paraffin embedded tissues were prepared. The slides were dried at 56°C for 1 hour, then deparaffinized with fresh xylene and rehydrated through ethanol washes. Antigen retrieval was performed by citrate buffer incubation (pH 6.0) using a microwave oven for 10 min at 100°C. Slides were incubated for 15 min with 3% hydrogen peroxide, washed in PBS, and incubated with an appropriate primary antibody followed by a secondary antibody. Sections were counterstained, and examined by fluorescence microscopy. Antibodies and dilutions used in immunocytochemistry were as follows: rabbit anti-PTEN (1:100); rabbit anti-RASSF1 (1:100); rabbit anti- PIK3R1 (1:100); rabbit anti IgG (1:400). The integrated optical density (IOD) was calculated for each sample [40]. For semi-quantitative analysis of the degree of staining, slides were independently scored by two pathologists. The scores were defined as follows: 0 (< 5% positive tumor cells); 1 (≤ 25% positive tumor cells); 2 (26–50% positive tumor cells); 3 (51–75% positive tumor cells); and 4 (> 75% positive tumor cells). Staining intensity was graded as: 0 (no staining); 1 (weak staining: light yellow); 2 (moderate staining: yellow brown); and 3 (strong staining: brown). Staining index (SI) was calculated as the product of staining intensity score and the proportion of positive tumor cells [41]. An SI score of 9–12 indicated strong positive (+++); 5–8 indicated positive (++); 1–4 indicated weakly positive (+); 0 indicated negative (−) staining.

Statistical analysis

We used the IBM SPSS Statistics 19.0 (IBM Corp., NY, USA) and the R program (https://www.r-project.org/) to analyze the data. Individual and cumulative methylation statuses of candidate genes were analyzed. We used the t-test, ANOVA or nonparametric test to compare the differences of methylation between groups. Considering the false positive caused by multiple comparisons, the Bonferroni correction was applied. The receiver operative characteristics (ROC) curve was drafted to reflect the diagnostic value of biomarkers. The area under the curve (AUC) together with 95% confidence interval (CI) were calculated.

SUPPLEMENTARY MATERIALS

Footnotes

CONFLICTS OF INTEREST

The authors declare that they have no competing interests.

FUNDING

This study is supported by the National Natural Science Foundation of China (81172268), Social Development Project in Jiangsu Province (BE2015694), Scientific Research Innovation Project for Graduate Student in Jiangsu Province (KYLX16_1125), Qing Lan Project (2014), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and Medical Science and Technology Development Foundation of Jiangsu (Y201313). The funding agencies had no role in the study design, data collection, analysis, decision to publish, or preparation of the manuscript.

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